@prefix this: . @prefix sub: . @prefix np: . @prefix rdf: . @prefix prov: . @prefix npx: . @prefix dc: . @prefix xsd: . sub:Head { this: a np:Nanopublication; np:hasAssertion sub:assertion; np:hasProvenance sub:provenance; np:hasPublicationInfo sub:pubinfo . } sub:assertion { a ; dc:description "Memory amount is the fraction of counted assignments in ODE_New.mat that fall in one of the five named kinds. Node count is Bin_numSp. Edges are half the sum of the deposited degree vector, mean degree is its mean, and hubness is its maximum divided by its mean, as pre-registered. Each is compared with memory amount by a two-sided Spearman correlation at alpha 0.05 with no multiple-comparison correction. The ODEs were not re-integrated."; "Measured: 0.017569332117588564 two-sided Spearman p for memory amount versus node count; all four tests are in extra (n = 35). Interpretation: The correlations are between simulation-derived memory scores and topology of the ODE models. They do not show that cellular memory is independent of network size. data_kind will be computational."; "Claim test of levin-2022-grn-learning-06: refutes, confidence 0.55"; ; prov:wasAttributedTo ; "The published ODE pathway models used for the memory assay, with topology counted from the same fetched model files that are simulated. A hand-typed table of the paper's correlations is not admissible."; "claim-test/0.1.0"; "computational"; "At least one of those four correlations has two-sided p < 0.05."; "Edges (p 0.080) and mean degree (p 0.503) are not significant at 0.05.", "Hubness here is maximum degree over mean degree. Two models have a degree vector one shorter than their node count (BIOMD0000000209, BIOMD0000000210). The node-count correlation does not use that vector.", "Memory amount correlates with node count (Spearman rho 0.399, two-sided p 0.0176) and with hubness (rho 0.346, p 0.0420). Either one fails the pre-registered rule that every correlation be non-significant.", "The ODEs were not re-integrated. Memory amount is a fraction of the deposited assignment counts."; "Spearman correlations of memory amount with node count, edge count, mean degree and hubness are all non-significant at two-sided p >= 0.05."; "Refutes if any of the four Spearman p-values is < 0.05. Inconclusive with reason underpowered if fewer than 15 models have a finite memory amount and finite topology measures. Inconclusive with reason data-unavailable if the models cannot be fetched. Mixed is not used."; "0.55"^^xsd:double; , ; "refutes"; "Across the surveyed BioModels pathway models, memory amount is not significantly correlated with node count, edge count, degree or hubness."; "Spearman rank correlation, two-sided, alpha 0.05, uncorrected, one test per measure. Supports only if every test is non-significant. n and each rho and p are reported from the script."; "Pre-registered before any model file or simulation output is downloaded. Memory amount is the fraction of tested stimulus-response pairs that show at least one of the five memory kinds defined in the levin-2022-grn-learning-01 preregistration. Degree is mean node degree. Hubness is maximum degree divided by mean degree, unless the authors' GRN_ODE code defines hubness, in which case that definition is used and named. Edges are the regulatory edges in the fetched model. The test is a two-sided Spearman rank correlation of memory amount with each of the four measures, alpha = 0.05, no multiple-comparison correction (the claim says no significant correlation with any of them, at the ordinary per-test bar). Supports if all four p-values are >= 0.05."; "false"^^xsd:boolean . . a ; dc:description "authors' MATLAB sources and deposited simulation-count matrices; the ODE right-hand sides are not in this repository"; dc:license ""; "Dataset: GRN_ODE, code and deposited outputs for Biswas, Clawson and Levin 2022"; prov:atLocation , , , , , , ; prov:wasDerivedFrom ; "sha256:0f4a71d2fca4c41c1dd91bcb7d1c3107fe754e2d596d8d2c4f4dfa26d402c771", "sha256:384a9740876e19d927593cb75af4b2632691616951f166840d01bc3bb16978db", "sha256:3ae6e7ccb729050f22c4f9f1b8feb587c2e05ab14166506392ab8e331e647574", "sha256:6768dd71632e319eded55cbab072eb79bc1af4510ef489e46be168e4828ac71e", "sha256:9b04ac49abb7ca558728f00f5048237688bf1a4acfd54b29b8c05659124bdffc", "sha256:b954fbb03601a7a4af543bb9c5d905a2b2c24a67995ee5e2f399ea1b8c7baa5e", "sha256:fb03c13849dccbc62a92dd9df087cd06075183e8a79dc391359cf0d6c7d98d6f" . a ; dc:description "BioModels ids in table order; each id was then resolved on the BioModels search API"; dc:license "cc by"; "Dataset: Table S1, list of 35 BioModels ODE networks"; prov:atLocation ; prov:wasDerivedFrom ; "sha256:1ae204459a539a8d52bbde46352c6e7fcbc7b28ae6833dcaa584585c35c7571c" . } sub:provenance { a prov:SoftwareAgent; "delphisci claim tester (grok-4.7; cursor/levin-xenobot-pathway-memory-254b)"; prov:actedOnBehalfOf ; . a ; dc:identifier "grok-4.7" . sub:assertion prov:wasAttributedTo ; prov:wasDerivedFrom ; prov:wasGeneratedBy sub:test . sub:plan prov:hadPlan . sub:test a prov:Activity; prov:qualifiedAssociation sub:plan; prov:wasAssociatedWith . } sub:pubinfo { this: dc:created "2026-11-08T16:24:31.873Z"^^xsd:dateTime; dc:creator ; dc:license ; npx:hasNanopubType ; npx:introduces ; "Claim test of levin-2022-grn-learning-06: refutes, confidence 0.55"; . sub:sig npx:hasAlgorithm "RSA"; npx:hasPublicKey "MIIBIjANBgkqhkiG9w0BAQEFAAOCAQ8AMIIBCgKCAQEAyFHtPB6p0mCjDozxlOY9CmO3nBm7Te3jFbXyLmM+G1tGe47Da0jYIbhkl9Oo1ZjxmCa3LCwlEqK8gj6hEIcL4ahW30JXh9HycfKVk1bugkzMEV15tJdTzAU3sGMFnbNsn43xY2LFLy45tApkRm5byEiM7oRRrooIdwoQKnP4Wzq/bfAoJ9qvW+MGkelyOTd1jbSt+KfmT7xKmDfWzAHddUlWwMX2QJOLxDaffOteTop6ZRgPiNmPJKA/S62Gac9XtSYT3oDkZ6/nr5cxxNLpOyxTf5FPISiCe11xYO/unYjY+mwnOfA8yhJIyx1l0O9I0vpOpn+E5TvyHJV+oSTrvwIDAQAB"; npx:hasSignature "YLk2fxB6V0L3SYB48dpAhrhZNN+9KoxZdgITRNoTo6spK/rv0a0wx4Zfg7itLAiImqXbyO1SpqpwqHd1/qAGweKHOR/jAMkQDzxR8euZuus1Xb8lIk3Q2jL1vIXWxwdvzZOYkwI+XiptxF7xiG8o/r4/nXLBKERbJxbYODWutvX5dsShp2o/CRrw2ls8Oc1g1ok2PMLs4PlVD4aeTleprsmZvLGGdkzZJ+5IfsGp1/ndIBS39BBNpDK6pfRr73NC+uDlmCyscI5m7HA9XBzhek9Czy4QEeaYlAt/qX0uYYqdjaUrCdIYJl+2cJSLB/nmN4qDZgOhlumRvt3nAvft+w=="; npx:hasSignatureTarget this:; npx:signedBy . }